morphiq-scan

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This skill should be used when the user asks to "audit a website for AI visibility", "scan a domain", "check AI readiness", "evaluate content quality", "run a Morphiq Scan", "check if a site is optimized for LLMs", or mentions scanning a website for LLM citation readiness. Performs a full AI visibility audit across 5 categories (agentic readiness, content quality, chunking & retrieval, query fanout, policy files) and scores the domain on a 100-point rubric.

2installs

Sourcemorphiqlabs/morphiq-labs-skills

Added on2026-03-30

NPX Install

npx skill4agent add morphiqlabs/morphiq-labs-skills morphiq-scan

SKILL.md Content

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Pipeline Position

Step 1 of 4 — entry point.

Input: A domain URL from the user.
Output: Scan Report (JSON) → consumed by morphiq-rank.
Data contract: See
```
PIPELINE.md
```
§1 for the Scan Report schema.

Purpose

Morphiq Scan audits a website's readiness for AI visibility. It answers two questions: "Can AI systems parse and understand this site?" (Technical Score) and "Where are the gaps preventing AI citations?" (issue identification). The output feeds morphiq-rank for prioritization.

Workflow

Step 1: Discover Pages

Fetch
```
{domain}/robots.txt
```
— extract sitemap URLs
Fetch
```
{domain}/sitemap.xml
```
if not found in robots.txt
Classify discovered pages by type using URL pattern matching
Select up to 10 marketing-relevant pages, prioritized: home → pricing → features → product → solutions → about → blog → other → documentation
Exclude non-marketing pages from scoring (contact, login, signup, legal, demo, careers, changelog)

For page type classification and URL patterns, read

references/page-type-rules.md

.

Step 2: Audit Policy Files (Category 5 — Domain Level)

robots.txt — Validate existence, format, AI crawler access (GPTBot, Google-Extended, Anthropic-AI, PerplexityBot)
llms.txt — Validate existence and quality (≥500 chars = good, <500 = thin)
llms-full.txt — Check existence
sitemap.xml — Validate XML structure

Score on 10-point scale. Generate issues for findings.

For detection rules and scoring, read

references/policy-files.md

.

Step 3: Score Each Page — Per-Page Technical Score (0–100)

For each selected page, compute across four dimensions:

Dimension	Points	Sub-checks
Schema	40	J1 (present), J2a (valid structure), J2b (required properties), J3 (relevant type), J4 (coverage)
Metadata	30	M1 (title), M2 (description), M3 (canonical), M4 (OG), M5 (Twitter)
FAQ	20	Linear scale: 0 FAQs=0, 1=5, 2=10, 3=15, 4+=20
Content	10	C1 (word count ≥300), C2 (≥3 paragraphs)

For sub-check methodology, read

references/agentic-readiness.md

.

Step 4: Score Content Quality (Category 2 — Per Page, 20 pts)

Evaluate citation-readiness: title clarity (3), TL;DR placement (4), E-E-A-T signals (6), statistics & citations (5), real examples (2).

For criteria, read

references/content-quality.md

.

Step 5: Score Chunking & Retrieval (Category 3 — Per Page, 15 pts)

Evaluate LLM retrieval optimization: heading hierarchy (3), section scope (3), paragraph self-containment (2.25), answer-first openings (2.25), vocabulary/lists/FAQ/summary (4.5).

For criteria, read

references/chunking-retrieval.md

.

Step 6: Simulate Query Fanout (Category 4 — Domain Level, 10 pts)

Identify core topics from page content
Generate simulated sub-queries using per-model rules (GPT-5.4 two-phase, Claude bundled, Gemini systematic)
Check site coverage per sub-query
Apply citation weights (citation-producing 1.5x, silent 0.5x,
```
site:
```
2x)

Score:

(weighted answered / weighted total) × 10

For simulation rules and coverage scoring, read

references/query-fanout.md

.

Step 7: Compute Aggregate Score (0–100)

overall = agentic_readiness(45) + content_quality(20) + chunking_retrieval(15) + query_fanout(10) + policy_files(10)

Categories 1–3 averaged across pages then scaled. Categories 4–5 are domain-level.

For the full rubric, read

references/scoring-rubric.md

.

Step 8: Generate Issues

Create issues with: id, category, severity, summary, detail, affected_urls, remediation_hint. Use issue ID patterns from the pipeline contract.

Step 9: Produce Scan Report

Assemble Scan Report JSON (

PIPELINE.md

§1): domain metadata, per-page scores, issues, schema detection, policy file status, query fanout analysis.

SaaS Detection

Before scoring, detect SaaS by matching 2+ of 3 content sources against indicator terms (platform, saas, cloud, api, etc.). Changes expected schemas for product/pricing/features/solutions pages.

For detection logic, read

references/page-type-rules.md

.

Reference Files

File	Purpose
`references/scoring-rubric.md`	Full 100-point rubric — Technical Score + 5-category pipeline model
`references/agentic-readiness.md`	Per-page Technical Score sub-checks (J1–J4, M1–M5, C1–C2, FAQ)
`references/page-type-rules.md`	19 page types, URL patterns, expected schemas, SaaS detection
`references/content-quality.md`	5 content quality pillars for citation-readiness
`references/chunking-retrieval.md`	10 evaluation areas for LLM retrieval optimization
`references/query-fanout.md`	Per-model fan-out simulation rules, citation weights, coverage scoring
`references/policy-files.md`	robots.txt + llms.txt detection, validation, and scoring